A data-driven approach for seismic damage detection of shear-type building structures using the fractal dimension of time-frequency features

In this study, a data-driven approach for structural seismic damage detection and localization in a multiple-degree-of-freedom shear-type building structure subjected to strong ground motion is presented. The proposed method is based on the joint implementation of time-frequency analysis and fractal dimension (FD) characteristics. The approximate analytical wavelet transform is first used to obtain the time-frequency feature (TFF) of the transient response at the measured story. The TFF is defined as the real part of the wavelet coefficients. Next, the box-counting method is used to acquire the FD of the TFF within the fundamental frequency band. It is verified that the proposed FDs at all stories of the linear system are identical, whereas the FDs at the stories with nonlinearities will be different from those at the stories with linearity. Therefore, the nonlinearity of the structure caused by strong ground motion can be detected and localized by comparing the FDs at the measured stories. A numerical simulation on a 10-story shear-type building was conducted. The simulation results indicate that the aforementioned approach is capable of detecting and localizing single-location or multiple-location seismic damages in shear-type building structures under various seismic excitations and is robust to measurement noise. Finally, the sensor placement for this approach, the effect of damping ratio, and choice of interstory drifts or accelerations as the measured signals are investigated and discussed. Copyright (c) 2012 John Wiley & Sons, Ltd.